Machine-Learning Anomaly Detection for Early Identification of DDoS in Smart Home IoT Devices

The rapid adoption of Internet of Things (IoT) devices in smart homes has introduced security vulnerabilities, with Distributed Denial of Service (DDoS) emerging as a critical threat. Exploiting the often-unsecured nature of these interconnected devices, such attacks overwhelm network resources, causing severe disruptions and privacy breaches. We present a novel anomaly detection system for early-stage DDoS attack identification in smart home IoT environments. Using NS-3 simulator, a realistic IoT network dataset was generated, capturing normal and malicious traffic. Key traffic features, e.g., packet size and inter-arrival times, were extracted to train two lightweight Machine Learning (ML) models: One-Class Support Vector Machine (OCSVM) and Isolation Forest (IF). OCSVM model achieved superior performance with accuracies from 96% to $99 \%$ for various attacks, while the IF model performed marginally worse. We offer a lightweight and scalable solution for real-time deployment in resource-constrained IoT environments, a significant step to enhance smart home security.